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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Momand, Mohammad Yusuf Vranic, Valentino Institute of Informatics Information System and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9798400700408

Digital transformation allows organizations to maintain sustainable development and address ongoing challenges. The current digital transformation and advanced technology mega-trend significantly impact society and organizations, making digital technology crucial for public and private organizations, universities, and daily life. Despite the rising demand, there is often a lack of standard policies governing the use of digital technology. Practical experience and literature reveal key challenges in transitioning from traditional to digital systems, including limited public awareness, mindset, ICT skills, government support, and collaboration between organizations and software engineering societies. Governments must establish digital transformation policies and offer software development guidelines for public organizations to digitalize their systems. Additionally, they should provide or support research projects funded by governmental and nongovernmental organizations to identify infrastructure and regulatory issues within Afghanistan’s digital transformation context. This study documents six organizational patterns observed during the transformation of traditional systems into digital systems, illustrating their sequence and explaining the development of a pattern language from these patterns. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： software design

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DiaSet: An Annotated Dataset of Arabic Conversations 30

DiaSet: An Annotated Dataset of Arabic Conversations

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Israeli, Abraham Naaman, Aviv Makhoul, Rawaa Maduel, Guy Ejmail, Amir Jubran, Julian Karain, Dana Lisnyansky, Dina Fine, Shai Bar, Kfir The Data Science Institute Reichman University Israel Department of Software and Information System Engineering Ben-Gurion University of the Negev Israel

ISBN: (纸本)9782493814104

We introduce DiaSet, a novel dataset of dialectical Arabic speech, manually transcribed and annotated for two specific downstream tasks: sentiment analysis and named entity recognition. The dataset encapsulates the Palestine dialect, predominantly spoken in Palestine, Israel, and Jordan. Our dataset incorporates authentic conversations between YouTube influencers and their respective guests. Furthermore, we have enriched the dataset with simulated conversations initiated by inviting participants from various locales within the said regions. The participants were encouraged to engage in dialogues with our interviewer. Overall, DiaSet consists of 644.8K tokens and 23.2K annotated instances. Uniform writing standards were upheld during the transcription process. Additionally, we established baseline models by leveraging some of the pre-existing Arabic BERT language models, showcasing the potential applications and efficiencies of our dataset. We make DiaSet publicly available for further research. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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Super-resolution reconstruction of single image for latent features

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Computational Visual Media 2024年第6期10卷 1219-1239页

作者： Xin Wang Jing-Ke Yan Jing-Ye Cai Jian-Hua Deng Qin Qin Yao Cheng School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610000China School of Marine Engineering Guilin University of Electronic TechnologyGuilin 541000China School of Computer and Information Security Guilin University of Electronic TechnologyGuilin 541000China

Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)***,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture *** challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model *** address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed *** then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising *** so boosts the generative modeling capabilities within a minimal number of sampling *** comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.

关键词： image superresolution reconstruction denoising diffusion probabilistic model normalized flow adversarial neural network variational auto-encoder

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Evaluating machine learning models and feature selection for reference evapotranspiration estimation in semi-arid regions: a case study in Doukkala, Morocco

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Theoretical and Applied Climatology 2025年第6期156卷 1-24页

作者： Belarbi, Zaid EL Younoussi, Yacine Information System and Software Engineering Laboratory Abdelmalek Essaadi University Tetouan Morocco

Accurate estimation of reference evapotranspiration (ETo) is crucial for effective water resource management and irrigation planning, particularly in regions with significant agricultural activity, such as Doukkala, Morocco. This study explores the potential of machine learning (ML) models to estimate daily ETo using 18 years of climatic data collected from two meteorological stations in the region. The dataset includes key meteorological variables—temperature, humidity, solar radiation, wind speed, and rainfall—with ETo calculated using the FAO-56 Penman-Monteith (PM) equation as the reference. A range of ML models, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), XGBoost, Random Forest (RF), k-Nearest Neighbors (KNN), Decision Trees (DT), and Multiple Linear Regression (MLR), were trained and compared. Additionally, deep learning architectures such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) were evaluated for their ability to capture temporal dependencies within the data. Among the tested models, ANN demonstrated the highest performance, achieving an R² of 0.9868 and an MSE of 0.0440, followed closely by SVR and XGBoost, both of which also exhibited high predictive accuracy. In contrast, simpler models like MLR and DT showed comparatively lower performance, underscoring the advantages of more advanced ML techniques for ETo estimation. While LSTM displayed moderate success, CNN underperformed relative to other models. The inclusion of data from two stations enabled the evaluation of model generalizability across distinct locations within the region. However, this study has some limitations. While cross-site validation improves generalizability, the findings are based on two stations within the same semi-arid region, which may not fully represent ETo dynamics in more diverse climatic conditions. Additionally, while ML models demonstrated strong predictive capabilities, further improvements could be achieved b

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Handwritten Prescription Recognition Using VGG Based Architecture with Bi-LSTM

Handwritten Prescription Recognition Using VGG Based Archite...

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2024 International Research Conference on Smart Computing and systems engineering, SCSE 2024

作者： Kavinda, Isuru Fernando, Harinda Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798350375688

Illegible handwriting on medical prescriptions poses a significant challenge, often leading to the misinterpretation of drug names and dosages. This issue primarily stems from doctors' use of Latin abbreviations, driven by time constraints during patient consultations. The resultant discrepancies in prescription interpretation can potentially expose patients to incorrect medications and dosages, leading to adverse effects. In response to this concern, this study proposes a novel system integrating Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) models. Promising findings are reported in this study, achieving a loss of 0.4874 and an accuracy of 0.83 in the latest trial. The final model offers a potential solution to the persistent problem of illegible prescriptions, enhancing patient safety in the healthcare domain. © 2024 IEEE.

关键词： Long short-term memory

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Development of Mathematical Methods and Algorithms for Filtering Images Obtained from Unmanned Aerial Vehicle Camera

Development of Mathematical Methods and Algorithms for Filte...

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2023 International Conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Zaw, Hein Htet Hein, Zaw Mikhailovich, Portnov E. National Research University of Electronic Technology Institute of System and Software Engineering and Information Technologies Zelenograd Moscow Russia

ISBN: (纸本)9781665475952

Recently, the use of unmanned aerial vehicles (UAVs) has become an interesting and active research topic in the field of aerial photography. Such an increase in popularity makes the task of finding optimal hardware and software configurations for UAVs, developing systems for compensating adverse environmental effects, and navigation systems in space, etc. The key factors behind this trend are the relatively low cost of implementing such projects and the speed of obtaining data. The main problems preventing full automation of data processing of UAV imagery are motion blur with a still camera, full image blur with camera movement, and camera focus blur. When shooting from a UAV in bad weather conditions, blurry video frames often occur that require image filter. This blurring can interfere with visual analysis and interpretation of data, cause errors, and reduce the accuracy of automatic photogrammetric processing algorithms. This article describes the proposed algorithm that performs element-by-element image filtering based on the use of neural-like structures. © 2023 IEEE.

关键词： Support vector machines

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Research on Adversarial Sample Attack Defense Methods for Speech Recognition 7

Research on Adversarial Sample Attack Defense Methods for Sp...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： He, Chunlan Bi, Yujian Liu, Xingwei Wang, Qi Sun, Hong School of Computer and Software Engineering Xihua University Chengdu China Chengdu Jiuzhou Electronic Information System Co. Ltd Chengdu China

ISBN: (纸本)9798350350890

Recently, deep learning has achieved significant success in various fields such as image classification, speech recognition, and natural language understanding. However, the security issues associated with deep learning are becoming increasingly apparent, particularly the challenges posed byad-versarial sample attacks. These attacks, which are both covert and potentially harmful, pose substantial security threats to deep learning models. In response to this, we propose a novel defense method against adversarial sample attacks tailored for speech recognition. This method involves constructing an audio reconstruction model that restores adversarial speech samples back to their normal state. The model incorporates an im-proved Wavenet residual network structure based on dilated convolutions, and utilizes Mel Frequency Cepstral Coefficients to enhance audio reconstruction. Experimental results indicate that the proposed audio reconstruction model exhibits superior reconstruction capabilities, effectively restoring and defending against various adversarial sample attacks. © 2024 IEEE.

关键词： Adversarial Defense Adversarial Speech Samples Audio Reconstruction Deep Learning Mel Frequency Cepstral Coefficients

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MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

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Computers, Materials & Continua 2025年第2期82卷 2969-2984页

作者： Ruoxin Li Shaoxiong Wu Fengping Deng Zhongli Tian Hua Cai Xiang Li Xu Xu Qi Liu NARI-TECH Nanjing Control System Co. Ltd.Nanjing211106China School of Software Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044China

Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.

关键词： Electrical load forecasting cloud computing smart grid weather attributes energy consumption time-series analysis

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Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

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Computers, Materials & Continua 2023年第3期74卷 5017-5033页

作者： Umair Muneer Butt Hadiqa Aman Ullah Sukumar Letchmunan Iqra Tariq Fadratul Hafinaz Hassan Tieng Wei Koh School of Computer Sciences Universiti Sains MalaysiaPenang1180Malaysia Department of Computer Science The University of ChenabGujrat50700Pakistan Department of Software Engineering and Information System Universiti Putra MalaysiaSelangor43400Malaysia

Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social ***,dynamic environments and anthropometric differences between individuals make it harder to recognize *** study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world *** uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural ***,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification *** state-of-the-art pre-trained models are exploited to find the best model for spatial feature *** temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture *** state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation *** addition,seven state-of-the-art optimizers are used to fine-tune the proposed network ***,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB *** contrast,the other uses optical flow ***,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets.

关键词： Human activity recognition deep learning transfer learning neural network ensemble learning spatio-temporal

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Metaverse Trend: Definition, Application, Opportunities, Law, and Ethics

Metaverse Trend: Definition, Application, Opportunities, Law...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Safitra, Muhammad Fakhrul Alhari, Muhammad Ilham Putri, Deyana Prastika Lubis, Muharman Fakhrurroja, Hanif Satria, Villy School of Industrial and System Engineering Telkom University Information System Department Bandung Indonesia Institut Teknologi Telkom Surabaya Information System Department Surabaya Indonesia School of Industrial and System Engineering Telkom University Industrial Engineering Department Bandung Indonesia School of Applied Science Telkom University Application Software Engineering Department Bandung Indonesia

ISBN: (纸本)9798350302684

The Metaverse concept pertains to an interconnected virtual realm comprised of virtual communities and spaces of virtual reality. In this domain, users can engage with each other through digital entities within a shared environment. This extensive digital domain harbors considerable potential to function as a versatile platform for diverse applications, encompassing gaming, social interaction, education, and commerce. The advancement of the Metaverse offers numerous prospects, including the creation of innovative modes of entertainment and communication, enabling remote collaboration, and ushering in novel business paradigms. However, the emergence of the Metaverse also gives rise to significant legal and ethical considerations, encompassing apprehensions surrounding privacy, intellectual property, and governance. To contend with these challenges, it is imperative to initially scrutinize extant literature concerning the Metaverse, its implementation, and to explore suggested solutions aimed at enhancing security and privacy. By amalgamating the technical framework of the Metaverse with its application state, we can glean insights into prevailing vulnerabilities and formulate appropriate security measures to shape the trajectory of this virtual realm. © 2023 IEEE.

关键词： Virtual reality

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